Advertisement

An Overview of Tabu Search Approaches to Production Scheduling Problems

  • J. Wesley Barnes
  • Manuel Laguna
  • Fred Glover
Chapter
Part of the Operations Research / Computer Science Interfaces Series book series (ORCS, volume 3)

Abstract

During the last five years, tabu search has been shown to be a remarkably effective method in solving difficult problems in the timely and very important area of production scheduling. In this paper, we present an overview of the research that has contributed to that success. We also review the various techniques that have been employed, giving attention to advances that have led to major improvements and suggesting directions for future research.

key words

tabu search and production scheduling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. Adams, J., E. Balas and D. Zawack, “The Shifting Bottleneck Procedure for Job Shop Scheduling”, Management Science, ,vol. 34, no. 3, March, 1988.CrossRefGoogle Scholar
  2. Applegate, D. and W. Cook, “A Computational Study of the Job Shop Scheduling Problem,”, ORSA Journal on Computing, ,Vol. 3, No. 2, 1991.CrossRefGoogle Scholar
  3. Applegate, D., Personal correspondence, 1991.Google Scholar
  4. Baker, Kenneth R. , Introduction to Sequencing and Scheduling, John Wiley Publishers, (1974).Google Scholar
  5. Baker, K.R. and G.D. Scudder, “Sequencing With Earliness and Tardiness Penalties: A Review”, Operations Research, vol. 38, pp. 22–36, 1990.CrossRefGoogle Scholar
  6. Balas, E., “Finding a Minimaximal Path in a Disjunctive PERT Network,”, Theorie des Graphes Journees internationales d’tude, Rome Juillet 1966, Dunod, Paris - Gordon and Beach, pp. 21–30, New York, 1967.Google Scholar
  7. Balas, E., “Machine Sequencing via Disjunctive Graphs: An Implicit Enumeration Algorithm”, Operations Research, vol. 17, pp. 941–957, 1969.CrossRefGoogle Scholar
  8. Barnes, J.W. and J. Brennan, “An Improved Algorithm for Scheduling Jobs on Identical Machines,”, AILE Transactions, vol. 9, no. 1, March, 1977.Google Scholar
  9. Barnes, J.W. and L. Vanston, “Scheduling Jobs with Linear Delay Penalties and Sequence Dependent Set-Up Costs,”, Journal of Operations Research, vol. 29, no. 1, January-February, 1981.CrossRefGoogle Scholar
  10. Barnes, J.W. and M. Laguna, “Solving the Multiple-Machine Weighted Flow Time Problem Using Tabu Search”, IIE Transactions, in press, 1992.Google Scholar
  11. Barnes, J.W. and J. Chambers, “Solving the Job Shop Scheduling Problem Using Tabu Search”, Technical Report Series, Graduate Program in Operations Research, The University of Texas at Austin, ORP91–06,1992.Google Scholar
  12. Beardwood, J., J.H. Halton, and J.M. Hammersley, “The Shortest Path through Many Points”, Proceedings of the Cambridge Philosophical Society, vol. 55, pp. 299–327, 1959.CrossRefGoogle Scholar
  13. Brandimarte, P„ “Routing and Scheduling in a Flexible Job Shop by Tabu Search,” Dipartimento di Sistemi di Produzione, Politecnico di Torino, Torino, Italy, 1992.Google Scholar
  14. Conway, R.W., W.L. Maxwell, and L.W. Miller, Theory of Scheduling, Addison-Wesley, Reading, Mass., 1970.Google Scholar
  15. de Werra, D. and A. Hertz, “Tabu Search Techniques: A Tutorial and an Application to Neural Networks”, OR Spectrum, vol. 11, pp. 131–141, 1989.CrossRefGoogle Scholar
  16. Dell’Amico, M. and M. Trubian, “Applying Tabu-Search to the Job-Shop Scheduling Problem,” to appear, Annals of Operations Research, 1992.Google Scholar
  17. Evans, J., “Structural Analysis of Local Search Heuristics in Combinatorial Optimization,”, Computers and Operations Research, vol. 14, no. 6, pp. 465–477, 1987.CrossRefGoogle Scholar
  18. Feo, T.F. and M. Resende, “A Probabilistic Heuristic for a Computationally Difficult Set Covering Problem”, Operations Research Letters, vol. 8, pp. 67–71, 1989.CrossRefGoogle Scholar
  19. Fiechter, C-N. “A Parallel Tabu Search Algorithm for Large Traveling Salesman Problems”, Research Report ORWP 90/1,Ecole Polytechnique Federale de Lausanne, Departement de Mathematiques, February, 1990.Google Scholar
  20. French, S., Sequencing and Scheduling, Ellis Horwood Limited, 1982.Google Scholar
  21. Gendreau, M., A. Hertz, and G. Laporte, “A Tabu Search Heuristic for the Vehicle Routing Problem,” Centre de recherche sur les transports, Universite de Montreal, publication #777, June 1991.Google Scholar
  22. Gendreau, M., A. Hertz, and G. Laporte, “New Insertion and Post-Optimization Procedures for the Traveling Salesman Problem,” Centre de recherche sur les transports, Universite de Montreal, CRT-708, 1990.Google Scholar
  23. Glover, F., “Heuristics for Integer Programming Using Surrogate Constraints”, Decision Sciences, vol 8, no. 1, pp. 156–166, 1977.CrossRefGoogle Scholar
  24. Glover, F., “Future Paths for Integer Programming and Links to Artificial Intelligence”, Computers and Operations Research, vol. 13, no. 5, pp. 533–549, 1986.CrossRefGoogle Scholar
  25. Glover, F., “Tabu Search - Part I”, ORSA Journal on Computing, vol. 1, no. 3, pp. 190–206, Summer 1989.CrossRefGoogle Scholar
  26. Glover, F., “Tabu Search - Part II”, ORSA Journal on Computing, vol. 2, no. 1, pp. 4–32, Winter 1990a.CrossRefGoogle Scholar
  27. Glover, F., “Tabu Search: A Tutorial”, Interfaces, vol. 20, no. 4, pp. 74–94, July - August 1990b.CrossRefGoogle Scholar
  28. Glover, F., “Multilevel Tabu Search and Embedded Search Neighborhoods for the Traveling Salesman Problem,” Graduate School of Business and Administration, University of Colorado at Boulder, June 1991.Google Scholar
  29. Glover, F. and M. Laguna, “Tabu Search”, University of Colorado at Boulder, 1992.Google Scholar
  30. Glover, F., E. Taillard, and D de Werra, “A User’s Guide to Tabu Search,” Graduate School of Business and Administration, University of Colorado at Boulder, November 1991.Google Scholar
  31. Hertz, A. and D. de Werra, “The Tabu Search Metaheuristic: How we use it”, Research Report ORWP 88/13, Ecole Polytechnique Federale de Lausanne, Departement de Mathematiques, March 1989, to appear in, Annals of Mathematics and Artificial Intelligence, .Google Scholar
  32. Johnson, D., “Local Optimization and the Traveling Salesman Problem,”, Proceedings of the 17th Annual Colloquim on Automata, Languages and Programming, Springer-Verlag, pp. 446–461, 1990.Google Scholar
  33. Knox, J. and F. Glover, “Tabu Search, An Effective Heuristic for Combinatorial Optimization Problems”, Center for Applied Artificial Intelligence, University of Colorado, Boulder, Colorado, March, 1988.Google Scholar
  34. Knox, J. and F. Glover, “Comparative Testing of Traveling Salesman Heuristics Derived form Tabu Search, Genetic Algorithms, and Simulated Annealing”, Center for Applied Artificial Intelligence, University of Colorado, Boulder, Colorado, September, 1989.Google Scholar
  35. Laguna, M., J. W. Barnes and F. Glover, “Tabu Search Methods for a Single Machine Scheduling Problem,”, Journal of Intelligent Manufacturing, vol. 2, pp. 63–73, 1991.CrossRefGoogle Scholar
  36. Laguna, M., and J. L. Gonzalez-Velarde, “A Search Heuristic for Just-in-Time Scheduling in Parallel Machines”, Journal of Intelligent Manufacturing, vol. 2, pp. 253–260, 1991.CrossRefGoogle Scholar
  37. Laguna, M., J. P. Kelly, J. L. Gonzalez-Velarde, and F. Glover, “Tabu Search for the Multilevel Generalized Assignment Problem,” Graduate School of Business and Administration, University of Colorado at Boulder, November 1991.Google Scholar
  38. Laguna, M., “Tabu Search Primer,” Graduate School of Business and Administration, University of Colorado at Boulder, March 1992.Google Scholar
  39. Laguna, M., J. W. Barnes and F. Glover, “Scheduling Jobs with Linear Delay Penalties and Sequence Dependent Setup Costs and Times Using Tabu Search”, Applied Intelligence, in press, 1992.Google Scholar
  40. Laguna, M. and F. Glover, “Integrating Target Analysis and Tabu Search for Improved Scheduling Systems”, Experts Systems with Applications: An International Journal, in press, 1992.Google Scholar
  41. Lam, J., “An Efficient Simulated Annealing Schedule,” PH.D. Dissertation, Report 8818, Department of Computer Science, Yale University, September 1988.Google Scholar
  42. Lawler, E.L., J.K. Lenstra, A. Rinnooy Kan, and D.B. Shmoys, “Sequencing and Scheduling: Algorithms and Complexity”, Report BS-R8909, Centre for Mathematics and Computer Science, Amsterdam, Netherlands, 1989.Google Scholar
  43. Lawler, E.L., J.K. Lenstra, A. Rinnooy Kan, and D.B. Shmoys, The traveling Salesman Problem, A Guided Tour of Combinatorial Optimization, Wiley-Interscience Pub., 1990.Google Scholar
  44. Lawrence, S., Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques, GSIA, Carnegie Mellon University, 1984.Google Scholar
  45. Lin, S. and B. Kernighan, “An Effective Heuristic Algorithm for the Traveling Salesman Problem,”, Operations Research, vol. 21, pp. 498–516, 1973.CrossRefGoogle Scholar
  46. Malek, M., M. Guruswamy, H. Owens, and M. Pandya, “Serial and Parallel Search Techniques for Traveling Salesman Problem”, Annals of OR: Linkages with Artificial Intelligence, vol. 21, pp. 59–84, 1989a.Google Scholar
  47. Malek, M., M. Guruswamy, H. Owens, and M. Pandya, “A Hybrid Algorithm Technique,” Report TR-89–06, Department of Computer Sciences, The University of Texas at Austin, Austin,Texas, March 1989bGoogle Scholar
  48. Matsuo, H., C. Suh, and R. Sullivan, “A Controlled Search Simulated Annealing Method for the General Jobshop Scheduling Problem,” Working Paper 03–44–88, Graduate School of Business, University of Texas at Austin.Google Scholar
  49. Morin, T.L. and R.E. Marsten, “Branch and Bound Strategies for Dynamic Programming”, Operations Research, vol. 24, no. 4, pp. 611–627, 1976.CrossRefGoogle Scholar
  50. Muhlenbein, H., “Parallel Genetic Algorithms and Combinatorial Optimization,” to appear, in SIAM Journal on Optimization, .Google Scholar
  51. Muth, J. and G. Thompson, Industrial Scheduling, Prentice-Hall, Englewood Cliffs, N.J., 1963.Google Scholar
  52. Nawaz, M., E. Enscore, and I. Ham, “A Heuristic Algorithm for the m-Machine, n-Job Flow Shop Sequencing Problem,”, OMEGA, vol. 11, no. 1, 1983.CrossRefGoogle Scholar
  53. Osman, I. H., “Metastrategy Simulated Annealing and Tabu Search Algorithms for the Vehicle Routing Problem,” to appear, in Annals of Operations Research, 1992.Google Scholar
  54. Padberg, M. and G. Rinaldi, “A Branch-and-Cut Algorithm for the Resolution of Large-Scale Symmetric Traveling Salesman Problems,” SIAM Review, vol. 33, no. 1, pp. 60–100, 1989.CrossRefGoogle Scholar
  55. Roy, B. and B. Sussman, “Les problemes d’ordonnancements avec contraintes disjonctives,” Notes DS n 9 bis, SEMA, Paris, 1964.Google Scholar
  56. Semet, F. and E. Taillard, “Solving Real-Life Vehicle Routing Problems Efficiently Using Tabu Search,” Ecole Polytechnique Fédérale de Lausanne, ORWP 91/03, April 1991.Google Scholar
  57. Stewart, W.R., Jr., “Accelerated Branch Exchange Heuristics for Symmetric Traveling Salesman Problems”, Networks, vol. 17, pp. 423–437, 1987.CrossRefGoogle Scholar
  58. Taïllard, E., “Parallel Taboo Search Technique for the Job Shop Scheduling Problem”, Research Report ORWP 89/11, Ecole Polytechnique Federale de Lausanne, Departement de Mathematiques, July 1989.Google Scholar
  59. Troyon, M., “Quelques Heuristiques et Resultats Asymptotiques por trois Problemes d’Optimisation Combinatoire,” These No. 754, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, 1988.Google Scholar
  60. Tsubakitani, S. and J. R. Evans, “Optimizing Tabu List Size for the Traveling Salesman Problem,” College of Business Administration, University of Cincinnati, June 1991.Google Scholar
  61. Ulder, N., E. Aarts, H. Bandelt, P. van Laarhoven, and E. Pesch, “Genetic Local Search Algorithms for the Traveling Salesman Problem,”, Lecture Notes in Computer Science, Vol. 496, Springer, Berlin, pp. 109–116, 1991.Google Scholar
  62. van Laarhoven, P., E. Aarts, and J. Lenstra, “Job Shop Scheduling by Simulated Annealing”, Report OS-R8809, Centrum voor Wiskunde en Informatica, Amsterdam, The Netherlands, 1988.Google Scholar
  63. Whitley, D., “Solving Generic Scheduling Problems Using Genetic Algorithms,” Technical Report: CIAI-TR-89–04, Colorado Institute of Artificial Intelligence, Boulder, Colorado, 1989.Google Scholar
  64. Widmer, M. and A. Hertz, “A New Method for the Flow Sequencing Problem”, European Journal of Operations Research, vol. 41, pp. 186–193, 1989.CrossRefGoogle Scholar
  65. Widmer, M., “Job Shop Scheduling with Tooling Constraints: a Tabu Search Approach,”, Journal of the Operational Research Society, vol. 24, no. 1, pp. 75–82, 1991.Google Scholar
  66. Woodruff, D. L. and M. L. Spearman, “Sequencing and Batching for Two Classes of Jobs with Deadlines and Setup Times,”, Journal of the Production and Operations Management Society, in press, 1992.Google Scholar
  67. Woodruff, D. L. and E. Zemel, “Hashing Vectors for Tabu Search,”, Annals of Operations Research, in press, 1992.Google Scholar

Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • J. Wesley Barnes
    • 1
  • Manuel Laguna
    • 2
  • Fred Glover
    • 3
  1. 1.Graduate Program in Operations Research and Industrial Engineering, Department of Mechanical Engineering, ETC 5.128DThe University of Texas at AustinAustinUSA
  2. 2.Graduate School of Business and AdministrationUniversity of Colorado at BoulderBoulderUSA
  3. 3.U S West Chair in Systems Science, Graduate School of Business and AdministrationUniversity of Colorado at BoulderBoulderUSA

Personalised recommendations